# A Sequence Matching Network for Polyphonic Sound Event Localization and Detection

arXiv: https://arxiv.org/abs/2002.05865 · HTML: https://mlsanity.com/paper/2002.05865/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events](https://mlsanity.com/paper/2306.09126/) (2023)
- [Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation](https://mlsanity.com/paper/2410.10676/) (2024)
- [Sound Event Detection and Localization with Distance Estimation](https://mlsanity.com/paper/2403.11827/) (2024)
- [SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform](https://mlsanity.com/paper/2106.06969/) (2021)
- [Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification](https://mlsanity.com/paper/2507.12042/) (2025)
- [Leveraging Visual Supervision for Array-based Active Speaker Detection and Localization](https://mlsanity.com/paper/2312.14021/) (2023)
- [What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis](https://mlsanity.com/paper/2107.10469/) (2021)
- [DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection](https://mlsanity.com/paper/2410.22803/) (2024)
- [Systematic Evaluation of Time-Frequency Features for Binaural Sound Source Localization](https://mlsanity.com/paper/2511.13487/) (2025)

## Abstract (arXiv)

Polyphonic sound event detection and direction-of-arrival estimation require different input features from audio signals. While sound event detection mainly relies on time-frequency patterns, direction-of-arrival estimation relies on magnitude or phase differences between microphones. Previous approaches use the same input features for sound event detection and direction-of-arrival estimation, and train the two tasks jointly or in a two-stage transfer-learning manner. We propose a two-step approach that decouples the learning of the sound event detection and directional-of-arrival estimation systems. In the first step, we detect the sound events and estimate the directions-of-arrival separately to optimize the performance of each system. In the second step, we train a deep neural network to match the two output sequences of the event detector and the direction-of-arrival estimator. This modular and hierarchical approach allows the flexibility in the system design, and increase the performance of the whole sound event localization and detection system. The experimental results using the DCASE 2019 sound event localization and detection dataset show an improved performance compared to the previous state-of-the-art solutions.